Abstract-- The recent success of deep neural networks (DNNs) for function approximation in reinforcement learning has t rig-gered the development of Deep Reinforcement Learning (DRL) algorithms in various fields, such as robotics, computer gam es, natural language processing, computer vision, sensing sys tems, and wireless networking. Unfortunately, DNNs suffer from h igh computational cost and memory consumption, which limits th e use of DRL algorithms in systems with limited hardware resources. In recent years, pruning algorithms have demonstrated cons id-erable success in reducing the redundancy of DNNs in classifi cation tasks. However, existing algorithms suffer from a sign ificant performance reduction in the DRL domain. In this paper, we develop the first effective solution to the performance redu ction problem of pruning in the DRL domain, and establish a working algorithm, named Policy Pruning and Shrinking (PoPS), to tr ain DRL models with strong performance while achieving a compac t representation of the DNN. The framework is based on a novel iterative policy pruning and shrinking method that leverag es the power of transfer learning when training the DRL model. We present an extensive experimental study that demonstrates the strong performance of PoPS using the popular Cartpole, Luna r Lander, Pong, and Pacman environments. Finally, we develop an open source software for the benefit of researchers and devel opers in related fields. Deep reinforcement learning (DRL) algorithms have attracted much attention in recent years due to their capabili ty to provide a good approximation of the objective value in decision making tasks while dealing with very large state an d action spaces. In contrast to classic reinforcement learni ng methods that perform well for small-size models but perform poorly for large-scale models, DRL combines a deep neural network (DNN) with reinforcement learning for overcoming this issue. The DNN is used to map from states to actions in large-scale models so as to yield a policy that maximizes the objective value. In DeepMind's recently published Natu re paper [1], [2], a DRL algorithm was developed to teach computers how to play Atari games directly from the on-scree n Personal use of this material is permitted. Dor Livne and Kobi Cohen are with the School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Shev a 8410501 Israel. This work was supported in part by the U.S.-Israel Binationa l Science Foundation (BSF) under grant 2017723, and by the Cyber Secur ity Research Center at Ben-Gurion University of the Negev under grant 076 /16.

With the rapid development of online education system, knowledge tracing which aims at predicting students' knowledge state is becoming a critical and fundamental task in personalized education. Traditionally, existing methods are domain-specified. However, there are a larger number of domains (e.g., subjects, schools) in the real world and the lacking of data in some domains, how to utilize the knowledge and information in other domains to help train a knowledge tracing model for target domains is increasingly important. We refer to this problem as domain adaptation for knowledge tracing (DAKT) which contains two aspects: (1) how to achieve great knowledge tracing performance in each domain. (2) how to transfer good performed knowledge tracing model between domains. To this end, in this paper, we propose a novel adaptable framework, namely adaptable knowledge tracing (AKT) to address the DAKT problem. Specifically, for the first aspect, we incorporate the educational characteristics (e.g., slip, guess, question texts) based on the deep knowledge tracing (DKT) to obtain a good performed knowledge tracing model. For the second aspect, we propose and adopt three domain adaptation processes. First, we pre-train an auto-encoder to select useful source instances for target model training. Second, we minimize the domain-specific knowledge state distribution discrepancy under maximum mean discrepancy (MMD) measurement to achieve domain adaptation. Third, we adopt fine-tuning to deal with the problem that the output dimension of source and target domain are different to make the model suitable for target domains. Extensive experimental results on two private datasets and seven public datasets clearly prove the effectiveness of AKT for great knowledge tracing performance and its superior transferable ability.

This paper proposes a new method for interpreting and simplifying a black box model of a deep random forest (RF) using a proposed rule elimination. In deep RF, a large number of decision trees are connected to multiple layers, thereby making an analysis difficult. It has a high performance similar to that of a deep neural network (DNN), but achieves a better generalizability. Therefore, in this study, we consider quantifying the feature contributions and frequency of the fully trained deep RF in the form of a decision rule set. The feature contributions provide a basis for determining how features affect the decision process in a rule set. Model simplification is achieved by eliminating unnecessary rules by measuring the feature contributions. Consequently, the simplified model has fewer parameters and rules than before. Experiment results have shown that a feature contribution analysis allows a black box model to be decomposed for quantitatively interpreting a rule set. The proposed method was successfully applied to various deep RF models and benchmark datasets while maintaining a robust performance despite the elimination of a large number of rules.

Neural Networks and Deep Learning by deeplearning.ai

Deep RL is where deep learning is used in conjunction with RL to simplify the reward function in cases where the search space is very large, or the environment is very complicated with multi-dimensional states, actions, and rewards. The use of deep learning with RL is also known as Q-learning in which a deep learning network is used as a function approximator (called the Q function), predicting the reward for an input, rather than trying to explore and store rewards and actions for every state. Also, in simulation environments, by simply feeding pixels of an environment through a neural network, it allows the reinforcement algorithm to better understand its environment. For the most part, RL is being used to teach AI systems how to play games, as games provide a safe and bounded environment for learning. For example, AlphaGo uses RL (in combination with other techniques) and similar techniques to have AI learn Atari games, or become champions at Poker.

One of the popular open-source libraries in machine learning, TensorFlow provides a suitable abode with essential tools for ML researchers and developers in order to perform SOTA machine learning applications. According to a survey, this library is one of the most loved deep learning frameworks. In this article, we list down 10 free resources to learn TensorFlow in 2020. About: Advanced Machine Learning (ML) with TensorFlow on Google Cloud Platform Specialization is a course in Coursera offered by Google Cloud. This course is a little advanced for beginners and is meant for those who already entered the machine learning arena. In this course, one can learn the hands-on experience in optimising, deploying, and scaling production ML models of various types.

German engineering company Robert Bosch GmbH is using artificial intelligence to reduce the risk of hackers tricking cars electronic systems into misinterpreting road signs. Engineering company Robert Bosch is deploying artificial intelligence (AI) to fortify cars' electronic systems against hackers who attempt to feed the systems intentionally incorrect road-sign information. Road-sign standardization makes traffic-sign recognition technology well-suited to machine learning and deep learning image-identification algorithms, but malefactors can deceive the algorithms by defacing the signs. Bosch's Michael Bolle said the company has unveiled a computer-vision-based AI process designed to analyze and compare an object from two different perspectives. The findings of deep learning algorithms that identify road signs are checked by computer-vision algorithms, and discrepancies between the readings could indicate spoofing.

Welcome to TechTalks' AI book reviews, a series of posts that explore the latest literature on AI. It wouldn't be an overstatement to say that artificial intelligence is one of the most confusing and least understood fields of science. On the one hand, we have headlines that warn of deep learning outperforming medical experts, creating their own language and spinning fake news stories. On the other hand, AI experts point out that artificial neural networks, the key innovation of current AI techniques, fail at some of the most basic tasks that any human child can perform. Artificial intelligence is also marked with some of the most divisive disputes and rivalries.

Oracle have been steadily and quietly working on AI for the better part of the last two decades. It started as an initiative to apply intelligent automation to back-end Oracle infrastructure maintenance.

Despite the variety of applications of AI in the clinical studies and healthcare services, they fall into two major categories: analysis of structured data, including images, genes and biomarkers, and analysis of unstructured data, such as notes, medical journals or patients' surveys to complement the structured data. The former approach is fueled by Machine Learning and Deep Learning Algorithms, while the latter rest on the specialized Natural Language Processing practices. ML algorithms chiefly extract features from data, such as patients' "traits" and medical outcomes of interest. For a long time, AI in healthcare was dominated by the logistic regression, the most simple and common algorithm when it is necessary to classify things. It was easy to use, quick to finish and easy to interpret.